import unittest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TextaTextGenerationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available

from .test_pipelines_common import ANY


if is_torch_available():
				import torch





@is_pipeline_test
class     lowerCAmelCase__     (				unittest.TestCase			):


		'''simple docstring'''






		lowerCAmelCase_     =   MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
		lowerCAmelCase_     =   TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
		def   _snake_case       (       self     :     str     ,  snake_case__     :     int     ,  snake_case__     :     Tuple     ,  snake_case__     :     Optional[int]		)      ->	str:
				_lowerCamelCase				   =  TextaTextGenerationPipeline(model=snake_case__     ,  tokenizer=snake_case__		)
				return generator, ["Something to write", "Something else"]
		def   _snake_case       (       self     :     List[Any]     ,  snake_case__     :     Union[str, Any]     ,  snake_case__     :     List[Any]		)      ->	str:
				_lowerCamelCase				   =  generator('Something there'		)
				self.assertEqual(snake_case__     ,  [{'generated_text': ANY(snake_case__		)}]		)
				# These are encoder decoder, they don't just append to incoming string
				self.assertFalse(outputs[0]['generated_text'].startswith('Something there'		)		)

				_lowerCamelCase				   =  generator(['This is great !', 'Something else']     ,  num_return_sequences=2     ,  do_sample=snake_case__		)
				self.assertEqual(
				    snake_case__     ,  [
				        [{'generated_text': ANY(snake_case__		)}, {'generated_text': ANY(snake_case__		)}],
				        [{'generated_text': ANY(snake_case__		)}, {'generated_text': ANY(snake_case__		)}],
				    ]     ,  )

				_lowerCamelCase				   =  generator(
				    ['This is great !', 'Something else']     ,  num_return_sequences=2     ,  batch_size=2     ,  do_sample=snake_case__		)
				self.assertEqual(
				    snake_case__     ,  [
				        [{'generated_text': ANY(snake_case__		)}, {'generated_text': ANY(snake_case__		)}],
				        [{'generated_text': ANY(snake_case__		)}, {'generated_text': ANY(snake_case__		)}],
				    ]     ,  )

				with self.assertRaises(snake_case__		):
						generator(4		)
		@require_torch
		def   _snake_case       (       self     :     str		)      ->	Dict:
				_lowerCamelCase				   =  pipeline('text2text-generation'     ,  model='patrickvonplaten/t5-tiny-random'     ,  framework='pt'		)
				# do_sample=False necessary for reproducibility
				_lowerCamelCase				   =  generator('Something there'     ,  do_sample=snake_case__		)
				self.assertEqual(snake_case__     ,  [{'generated_text': ''}]		)

				_lowerCamelCase				   =  3
				_lowerCamelCase				   =  generator(
				    'Something there'     ,  num_return_sequences=snake_case__     ,  num_beams=snake_case__     ,  )
				_lowerCamelCase				   =  [
				    {'generated_text': 'Beide Beide Beide Beide Beide Beide Beide Beide Beide'},
				    {'generated_text': 'Beide Beide Beide Beide Beide Beide Beide Beide'},
				    {'generated_text': ''},
				]
				self.assertEqual(snake_case__     ,  snake_case__		)

				_lowerCamelCase				   =  generator('This is a test'     ,  do_sample=snake_case__     ,  num_return_sequences=2     ,  return_tensors=snake_case__		)
				self.assertEqual(
				    snake_case__     ,  [
				        {'generated_token_ids': ANY(torch.Tensor		)},
				        {'generated_token_ids': ANY(torch.Tensor		)},
				    ]     ,  )
				_lowerCamelCase				   =  generator.model.config.eos_token_id
				_lowerCamelCase				   =  '<pad>'
				_lowerCamelCase				   =  generator(
				    ['This is a test', 'This is a second test']     ,  do_sample=snake_case__     ,  num_return_sequences=2     ,  batch_size=2     ,  return_tensors=snake_case__     ,  )
				self.assertEqual(
				    snake_case__     ,  [
				        [
				            {'generated_token_ids': ANY(torch.Tensor		)},
				            {'generated_token_ids': ANY(torch.Tensor		)},
				        ],
				        [
				            {'generated_token_ids': ANY(torch.Tensor		)},
				            {'generated_token_ids': ANY(torch.Tensor		)},
				        ],
				    ]     ,  )



		@require_tf
		def   _snake_case       (       self     :     str		)      ->	Dict:
				_lowerCamelCase				   =  pipeline('text2text-generation'     ,  model='patrickvonplaten/t5-tiny-random'     ,  framework='tf'		)
				# do_sample=False necessary for reproducibility
				_lowerCamelCase				   =  generator('Something there'     ,  do_sample=snake_case__		)
				self.assertEqual(snake_case__     ,  [{'generated_text': ''}]		)
A						=			'\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'

A						=			[{'type': 'code', 'content': INSTALL_CONTENT}]
A						=			{
    '{processor_class}': 'FakeProcessorClass',
    '{model_class}': 'FakeModelClass',
    '{object_class}': 'FakeObjectClass',
}